It has been shown that this method provides an estimate of the error that is very close to that one could obtain on an independent evaluation set ( (Varma and Simon, 2006), (Scheffer, 1999)), as it prevents us from optimizing our hyper-parameters and performing evaluation on the same data.
there are two cross-validation loops: the inner loop is used for tuning the hyper-parameters (as described in section 6.2) and the outer loop estimates the generalization error.