diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index adcf48507698b..c9954eb2c1c2f 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -92,7 +92,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.arrays.IntervalArray.length SA01" \
         -i "pandas.arrays.IntervalArray.right SA01" \
         -i "pandas.arrays.NumpyExtensionArray SA01" \
-        -i "pandas.arrays.SparseArray PR07,SA01" \
         -i "pandas.arrays.TimedeltaArray PR07,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.boxplot PR07,RT03,SA01" \
         -i "pandas.core.groupby.DataFrameGroupBy.get_group RT03,SA01" \
diff --git a/pandas/core/arrays/sparse/array.py b/pandas/core/arrays/sparse/array.py
index a3db7dc1f93e9..137dbb6e4d139 100644
--- a/pandas/core/arrays/sparse/array.py
+++ b/pandas/core/arrays/sparse/array.py
@@ -289,12 +289,18 @@ class SparseArray(OpsMixin, PandasObject, ExtensionArray):
     """
     An ExtensionArray for storing sparse data.
 
+    SparseArray efficiently stores data with a high frequency of a
+    specific fill value (e.g., zeros), saving memory by only retaining
+    non-fill elements and their indices. This class is particularly
+    useful for large datasets where most values are redundant.
+
     Parameters
     ----------
     data : array-like or scalar
         A dense array of values to store in the SparseArray. This may contain
         `fill_value`.
     sparse_index : SparseIndex, optional
+        Index indicating the locations of sparse elements.
     fill_value : scalar, optional
         Elements in data that are ``fill_value`` are not stored in the
         SparseArray. For memory savings, this should be the most common value
@@ -345,6 +351,10 @@ class SparseArray(OpsMixin, PandasObject, ExtensionArray):
     -------
     None
 
+    See Also
+    --------
+    SparseDtype : Dtype for sparse data.
+
     Examples
     --------
     >>> from pandas.arrays import SparseArray