The `score` isn't necessarily suitable for the estimator, nor for the problem; e.g. LR's score is accuracy, but what it's optimizing is minus the log-loss (OvR) and what you want to use it for could be F1 score, the true multiclass log loss or any other classification metric.

So, I propose:

> Estimators define a `score` method that provides a default evaluation criterion for the problem they are designed to solve (usually accuracy for classifiers, mean squared error for regressors).