Despite numerous successes of the kernel ridge regression,it remains largely unclear how to select regularization parameter in different scenarios.The aim of this paper is to systematically study such regularization parameter selection problem for the kernel ridge regression.We theoretically compare the performance of two types of popular selection methods including frequentist approaches and Bayesian approaches in the literature.