Cross validation is widely employed to check a statistical model and a prior distribution,  however, it is known that cross validation error has an inverse correlation with the generalization error even if a posterior distribution is in a singular state. In this talk, I will discuss bias and variance of Bayes cross validation and show that there is a more accurate method for estimating generalization error than leave-one-out cross validation. Several experimental results are demonstrated.