diff --git a/sklearn/linear_model/stochastic_gradient.py b/sklearn/linear_model/stochastic_gradient.py
index 168940b7d5d6f49f69a2e6afd07c3c3d190b3d85..6ea44a135202624d8f329942b06ed693af51560b 100644
--- a/sklearn/linear_model/stochastic_gradient.py
+++ b/sklearn/linear_model/stochastic_gradient.py
@@ -974,7 +974,7 @@ class SGDRegressor(BaseSGDRegressor, _LearntSelectorMixin):
     penalty : str, 'l2' or 'l1' or 'elasticnet'
         The penalty (aka regularization term) to be used. Defaults to 'l2'
         which is the standard regularizer for linear SVM models. 'l1' and
-        'elasticnet' migh bring sparsity to the model (feature selection)
+        'elasticnet' might bring sparsity to the model (feature selection)
         not achievable with 'l2'.
 
     alpha : float