Kernel methods provide a principled approach to nonparametric learning. While their basic implementations scale poorly to large problems, recent advances showed that approximate solvers can efficiently handle massive datasets.A shortcoming of these solutions is that hyperparameter tuning is not taken care of, and left for the user to perform.Hyperparameters are crucial in practice and the lack of automated tuning greatly hinders efficiency and usability. In this paper, we work to fill in this gap focusing on kernel ridge regression based on the Nystr\"om approximation.After reviewing and contrasting a number of hyperparameter tuning strategies,we propose a complexity regularization criterion based on a data dependent penalty, and discuss its efficient optimization.Then, we proceed to a careful and extensive empirical evaluation highlighting strengths and weaknesses of the different tuning strategies. Our analysis shows the benefit of the proposed approach, that we hence incorporate in a library for large scale kernel methods to derive adaptively tuned solutions.