K-fold cross-validation has a computational advantage over which method?

K-fold cross-validation offers a computational advantage primarily over leave-one-out cross-validation. In leave-one-out cross-validation, the model is trained multiple times, where each time it leaves out just one instance from the training set. For a dataset with a large number of samples, this results in a prohibitively high number of distinct training runs, leading to considerable computational overhead.

On the other hand, k-fold cross-validation divides the data into k subsets or "folds." The model is then trained k times, with each fold serving as a validation set once while the remaining k-1 folds are used for training. This approach generally requires fewer training iterations than leave-one-out cross-validation, as it balances the need for model evaluation with the computational efficiency of training the model less frequently.

In contrast to leave-one-out cross-validation, using a single validation set does not involve repeatedly training the model; rather, the model is typically trained once on a larger subset of the data while using the validation set to test its performance. Thus, k-fold cross-validation provides a more efficient means of model validation, especially with larger datasets, while maintaining the robustness of multiple evaluations.