[MRG] add crossvalidation to overfitting/underfitting example

Improve the plot_underfitting_overfitting.py example by adding a quantitative measure using crossvalidation
what do you mean? the model is trained on the training data, but then the MSE refers to the validation set, no? Do you think it would be more simple for the reader if we phrase:  

    "We evaluate quantitavely **overfitting** / **underfitting** by using   
    cross-validation. We calculate the mean squared error (MSE) score,   
    the higher, the less likely the model describes correctly the data."