As seen in the accepted answer for variance of k-fold cross validation , the simulation shows that k-fold CV has the same test error rate for different values of k when n=200. Does this mean that k-fold validation is likely to be as good as having a holdout set validation? (assuming I have abundant data to make up for the high bias for holdout set validation approach)
Apart from high bias, the problem with holdout set validation approach as described in ISL book is that the test error rate is sensitive to the random splitting of data between train and validate. My intuition is that, With very high n (and well spread out data), the problem due to random splitting seems less likely to occur.