diff --git a/pandas/core/arrays/base.py b/pandas/core/arrays/base.py
index 834c2db9ecb21..d489fafeaa308 100644
--- a/pandas/core/arrays/base.py
+++ b/pandas/core/arrays/base.py
@@ -172,7 +172,7 @@ class ExtensionArray:
     * _formatter : Print scalars inside a Series or DataFrame.
 
     Some methods require casting the ExtensionArray to an ndarray of Python
-    objects with ``self.astype(object)``, which may be expensive. When
+    objects with ``self.to_numpy(object)``, which may be expensive. When
     performance is a concern, we highly recommend overriding the following
     methods:
 
@@ -780,7 +780,7 @@ def fillna(
         if mask.any():
             if method is not None:
                 func = missing.get_fill_func(method)
-                new_values, _ = func(self.astype(object), limit=limit, mask=mask)
+                new_values, _ = func(self.to_numpy(object), limit=limit, mask=mask)
                 new_values = self._from_sequence(new_values, dtype=self.dtype)
             else:
                 # fill with value
@@ -859,7 +859,7 @@ def unique(self: ExtensionArrayT) -> ExtensionArrayT:
         -------
         uniques : ExtensionArray
         """
-        uniques = unique(self.astype(object))
+        uniques = unique(self.to_numpy(object))
         return self._from_sequence(uniques, dtype=self.dtype)
 
     def searchsorted(
@@ -911,7 +911,7 @@ def searchsorted(
         # 1. Values outside the range of the `data_for_sorting` fixture
         # 2. Values between the values in the `data_for_sorting` fixture
         # 3. Missing values.
-        arr = self.astype(object)
+        arr = self.to_numpy(object)
         if isinstance(value, ExtensionArray):
             value = value.astype(object)
         return arr.searchsorted(value, side=side, sorter=sorter)
@@ -988,7 +988,7 @@ def _values_for_factorize(self) -> tuple[np.ndarray, Any]:
         The values returned by this method are also used in
         :func:`pandas.util.hash_pandas_object`.
         """
-        return self.astype(object), np.nan
+        return self.to_numpy(object), np.nan
 
     def factorize(self, na_sentinel: int = -1) -> tuple[np.ndarray, ExtensionArray]:
         """
@@ -1166,7 +1166,7 @@ def take(self, indices, allow_fill=False, fill_value=None):
 
                # If the ExtensionArray is backed by an ndarray, then
                # just pass that here instead of coercing to object.
-               data = self.astype(object)
+               data = self.to_numpy(object)
 
                if allow_fill and fill_value is None:
                    fill_value = self.dtype.na_value
@@ -1503,7 +1503,7 @@ def _fill_mask_inplace(
         func = missing.get_fill_func(method)
         # NB: if we don't copy mask here, it may be altered inplace, which
         #  would mess up the `self[mask] = ...` below.
-        new_values, _ = func(self.astype(object), limit=limit, mask=mask.copy())
+        new_values, _ = func(self.to_numpy(object), limit=limit, mask=mask.copy())
         new_values = self._from_sequence(new_values, dtype=self.dtype)
         self[mask] = new_values[mask]
         return
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
index 2f26f6cc22f80..3284cfed354e1 100644
--- a/pandas/core/arrays/datetimelike.py
+++ b/pandas/core/arrays/datetimelike.py
@@ -314,7 +314,7 @@ def _formatter(self, boxed: bool = False):
     def __array__(self, dtype: NpDtype | None = None) -> np.ndarray:
         # used for Timedelta/DatetimeArray, overwritten by PeriodArray
         if is_object_dtype(dtype):
-            return np.array(list(self), dtype=object)
+            return self.astype(object)
         return self._ndarray
 
     @overload
@@ -816,14 +816,14 @@ def isin(self, values) -> npt.NDArray[np.bool_]:
                         pass
 
                     elif "mixed" in inferred:
-                        return isin(self.astype(object), values)
+                        return isin(self.to_numpy(object), values)
                     else:
                         return np.zeros(self.shape, dtype=bool)
 
             try:
                 values = type(self)._from_sequence(values)
             except ValueError:
-                return isin(self.astype(object), values)
+                return isin(self.to_numpy(object), values)
 
         try:
             self._check_compatible_with(values)
@@ -1022,7 +1022,7 @@ def _cmp_method(self, other, op):
             #  comparing tz-aware and tz-naive
             with np.errstate(all="ignore"):
                 result = ops.comp_method_OBJECT_ARRAY(
-                    op, np.asarray(self.astype(object)), other
+                    op, np.asarray(self.to_numpy(object)), other
                 )
             return result
 
diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py
index 9ceded00bf159..ef85824ff0eaf 100644
--- a/pandas/core/arrays/interval.py
+++ b/pandas/core/arrays/interval.py
@@ -1661,7 +1661,7 @@ def isin(self, values) -> np.ndarray:
                 # not comparable -> no overlap
                 return np.zeros(self.shape, dtype=bool)
 
-        return isin(self.astype(object), values.astype(object))
+        return isin(self.to_numpy(object), values.astype(object))
 
     @property
     def _combined(self) -> ArrayLike:
diff --git a/pandas/core/arrays/period.py b/pandas/core/arrays/period.py
index 04cc70b7efa6a..80e8c605e2bec 100644
--- a/pandas/core/arrays/period.py
+++ b/pandas/core/arrays/period.py
@@ -638,7 +638,7 @@ def _format_native_types(
         """
         actually format my specific types
         """
-        values = self.astype(object)
+        values = self.to_numpy(object)
 
         if date_format:
             formatter = lambda dt: dt.strftime(date_format)