Sci-kit: What's the easiest way to get the confusion matrix of an estimator when using GridSearchCV?

You will first need to predict using best estimator in your GridSerarchCV. A common method to use is GridSearchCV.decision_function(), But for your example, decision_function returns class probabilities from LogisticRegression and does not work with confusion_matrix. Instead, find best estimator using lr_gs and predict the labels using that estimator.

y_pred = lr_gs.best_estimator_.predict(X)

Finally, use sklearn's confusion_matrix on real and predicted y

from sklearn.metrics import confusion_matrix
print confusion_matrix(y, y_pred)