I always wondered how CalibratedClassifierCV was supposed to achieve probability calibration without a dedicated calibration set (which is appealing since no data is lost for training the classifier). Only when I looked at its documentation in detail I realized that what is called cross validation there is not really cross validation but rather an ensemble method, where simply k classifiers and k associated regressors are trained, whose average results are returned. Is that correct? And why is it not clearly stated in the documentation? I feel this greatly hampers understanding what this class actually does.