# Can we say: the more we increase the numbers of cross validation the less likely it is that we overfit?

Based on the answer of my previous question: How can I avoid overfitting when doing parameter tuning?

Can we say: the more we increase the numbers K of cross validation the less likely it is that we overfit?

As an example, if you have $$N$$ folds for $$N$$ datapoints, each fold will have only a single datapoint in its validation set. The validation accuracy of a model on a single datapoint is not a reliable estimator for the test performance of the model. In fact, you can construct examples where the error is arbitrarily large.