[MRG] add crossvalidation to overfitting/underfitting example

Improve the plot_underfitting_overfitting.py example by adding a quantitative measure using crossvalidation
In particular, a model that overfits "describes" correct the training data (as it memorized it) but will have poor CV score (poor generalization).

A model that underfits a lot does not "describe" the training data nor does it generalize (because it has nothing interesting to generalize).