Cross-validation assesses a model's ability to predict new testing data that was not used in training the model

Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample

Cross-validation involves partitioning the original dataset into training and testing subsets, and often one round of such partition is sufficient

Cross-validation can be used to tune parameters of the model