title: One standard error rule for cross-validation in scikit-learn
body: <p>I'm trying to fit some models in scikit-learn using grisSearchCV, and I would like to use the "one standard error" rule to select the best model, i.e. selecting the most parsimonious model  from the subset of models whose score is within one standard error of the best score. Is there  a way to do this?</p>