diff --git a/sklearn/preprocessing/data.py b/sklearn/preprocessing/data.py
index 8c8524ef6505c..639e4234f500d 100644
--- a/sklearn/preprocessing/data.py
+++ b/sklearn/preprocessing/data.py
@@ -1475,17 +1475,21 @@ def transform(self, X):
 
         Parameters
         ----------
-        X : array-like or sparse matrix, shape [n_samples, n_features]
+        X : array-like or CSR/CSC sparse matrix, shape [n_samples, n_features]
             The data to transform, row by row.
-            Sparse input should preferably be in CSR format (for speed),
-            but must be in CSC format if the degree is 4 or higher.
-
-            If the input matrix is in CSR format and the expansion is of
-            degree 2 or 3, the method described in the work "Leveraging
-            Sparsity to Speed Up Polynomial Feature Expansions of CSR
-            Matrices Using K-Simplex Numbers" by Andrew Nystrom and
-            John Hughes is used, which is much faster than the method
-            used on CSC input.
+
+            Prefer CSR over CSC for sparse input (for speed), but CSC is
+            required if the degree is 4 or higher. If the degree is less than
+            4 and the input format is CSC, it will be converted to CSR, have
+            its polynomial features generated, then converted back to CSC.
+
+            If the degree is 2 or 3, the method described in "Leveraging
+            Sparsity to Speed Up Polynomial Feature Expansions of CSR Matrices
+            Using K-Simplex Numbers" by Andrew Nystrom and John Hughes is
+            used, which is much faster than the method used on CSC input. For
+            this reason, a CSC input will be converted to CSR, and the output
+            will be converted back to CSC prior to being returned, hence the
+            preference of CSR.
 
         Returns
         -------