In "k"-fold cross-validation, the original sample is randomly partitioned into "k" equal sized subsamples. Of the "k" subsamples, a single subsample is retained as the validation data for testing the model, and the remaining "k" − 1 subsamples are used as training data. The cross-validation process is then repeated "k" times, with each of the "k" subsamples used exactly once as the validation data. The "k" results can then be averaged to produce a single estimation. The advantage of this method over repeated random sub-sampling (see below) is that all observations are used for both training and validation, and each observation is used for validation exactly once. 10-fold cross-validation is commonly used, but in general "k" remains an unfixed parameter.