diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index 857a2d3216b89..2998d8470d36d 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -245,7 +245,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.truediv PR07" \
         -i "pandas.Series.update PR07,SA01" \
         -i "pandas.Series.var PR01,RT03,SA01" \
-        -i "pandas.SparseDtype SA01" \
         -i "pandas.Timedelta PR07,SA01" \
         -i "pandas.Timedelta.as_unit SA01" \
         -i "pandas.Timedelta.asm8 SA01" \
diff --git a/pandas/core/dtypes/dtypes.py b/pandas/core/dtypes/dtypes.py
index dc7e9afac331b..e10080604260a 100644
--- a/pandas/core/dtypes/dtypes.py
+++ b/pandas/core/dtypes/dtypes.py
@@ -1665,7 +1665,10 @@ class SparseDtype(ExtensionDtype):
     """
     Dtype for data stored in :class:`SparseArray`.
 
-    This dtype implements the pandas ExtensionDtype interface.
+    `SparseDtype` is used as the data type for :class:`SparseArray`, enabling
+    more efficient storage of data that contains a significant number of
+    repetitive values typically represented by a fill value. It supports any
+    scalar dtype as the underlying data type of the non-fill values.
 
     Parameters
     ----------
@@ -1695,6 +1698,11 @@ class SparseDtype(ExtensionDtype):
     -------
     None
 
+    See Also
+    --------
+    arrays.SparseArray : The array structure that uses SparseDtype
+        for data representation.
+
     Examples
     --------
     >>> ser = pd.Series([1, 0, 0], dtype=pd.SparseDtype(dtype=int, fill_value=0))