diff --git a/ci/code_checks.sh b/ci/code_checks.sh
index f2d9f582d8932..35e23f8d7c531 100755
--- a/ci/code_checks.sh
+++ b/ci/code_checks.sh
@@ -97,7 +97,6 @@ if [[ -z "$CHECK" || "$CHECK" == "docstrings" ]]; then
         -i "pandas.Series.dt.tz_localize PR01,PR02" \
         -i "pandas.Series.dt.unit GL08" \
         -i "pandas.Series.pad PR01,SA01" \
-        -i "pandas.Series.sparse.fill_value SA01" \
         -i "pandas.Series.sparse.from_coo PR07,SA01" \
         -i "pandas.Series.sparse.npoints SA01" \
         -i "pandas.Series.sparse.sp_values SA01" \
diff --git a/pandas/core/arrays/sparse/array.py b/pandas/core/arrays/sparse/array.py
index a09dc20af3b36..7504a9bab8544 100644
--- a/pandas/core/arrays/sparse/array.py
+++ b/pandas/core/arrays/sparse/array.py
@@ -623,6 +623,12 @@ def fill_value(self):
 
         For memory savings, this should be the most common value in the array.
 
+        See Also
+        --------
+        SparseDtype : Dtype for data stored in :class:`SparseArray`.
+        Series.value_counts : Return a Series containing counts of unique values.
+        Series.fillna : Fill NA/NaN in a Series with a specified value.
+
         Examples
         --------
         >>> ser = pd.Series([0, 0, 2, 2, 2], dtype="Sparse[int]")