Less data samples can be helpful for the model training.

Higher k value of K-fold methods can be used. (e.g.,  k = 10, instead of k=5)

A less powerful, but a sufficient model in performance can be used.

In Decision Trees, large trees with more maximum depths could be helpful.

Regularization methods can be used.

Bootstrap aggregating (bagging) is helpful with randomized bootstrap samples.