In machine learning, a technique called cross-validation is used to assess a model’s performance on omitted data. The data is divided into several folds, the model is tested on certain folds and trained on others, and the outcomes are averaged. By doing so, overfitting is less likely to occur and the model’s performance is estimated more accurately.