diff --git a/pandas/core/generic.py b/pandas/core/generic.py
index 85bd6065314f4..dd5552151f61b 100644
--- a/pandas/core/generic.py
+++ b/pandas/core/generic.py
@@ -1303,8 +1303,85 @@ def __invert__(self):
 
     def equals(self, other):
         """
-        Determines if two NDFrame objects contain the same elements. NaNs in
-        the same location are considered equal.
+        Test whether two objects contain the same elements.
+
+        This function allows two Series or DataFrames to be compared against
+        each other to see if they have the same shape and elements. NaNs in
+        the same location are considered equal. The column headers do not
+        need to have the same type, but the elements within the columns must
+        be the same dtype.
+
+        Parameters
+        ----------
+        other : Series or DataFrame
+            The other Series or DataFrame to be compared with the first.
+
+        Returns
+        -------
+        bool
+            True if all elements are the same in both objects, False
+            otherwise.
+
+        See Also
+        --------
+        Series.eq : Compare two Series objects of the same length
+            and return a Series where each element is True if the element
+            in each Series is equal, False otherwise.
+        DataFrame.eq : Compare two DataFrame objects of the same shape and
+            return a DataFrame where each element is True if the respective
+            element in each DataFrame is equal, False otherwise.
+        assert_series_equal : Return True if left and right Series are equal,
+            False otherwise.
+        assert_frame_equal : Return True if left and right DataFrames are
+            equal, False otherwise.
+        numpy.array_equal : Return True if two arrays have the same shape
+            and elements, False otherwise.
+
+        Notes
+        -----
+        This function requires that the elements have the same dtype as their
+        respective elements in the other Series or DataFrame. However, the
+        column labels do not need to have the same type, as long as they are
+        still considered equal.
+
+        Examples
+        --------
+        >>> df = pd.DataFrame({1: [10], 2: [20]})
+        >>> df
+            1   2
+        0  10  20
+
+        DataFrames df and exactly_equal have the same types and values for
+        their elements and column labels, which will return True.
+
+        >>> exactly_equal = pd.DataFrame({1: [10], 2: [20]})
+        >>> exactly_equal
+            1   2
+        0  10  20
+        >>> df.equals(exactly_equal)
+        True
+
+        DataFrames df and different_column_type have the same element
+        types and values, but have different types for the column labels,
+        which will still return True.
+
+        >>> different_column_type = pd.DataFrame({1.0: [10], 2.0: [20]})
+        >>> different_column_type
+           1.0  2.0
+        0   10   20
+        >>> df.equals(different_column_type)
+        True
+
+        DataFrames df and different_data_type have different types for the
+        same values for their elements, and will return False even though
+        their column labels are the same values and types.
+
+        >>> different_data_type = pd.DataFrame({1: [10.0], 2: [20.0]})
+        >>> different_data_type
+              1     2
+        0  10.0  20.0
+        >>> df.equals(different_data_type)
+        False
         """
         if not isinstance(other, self._constructor):
             return False