k-fold cross-validation is a statistical method used to assess the performance of a machine learning model by dividing the dataset into 'k' subsets, or folds. In this technique, the model is trained on 'k-1' folds and validated on the remaining fold, repeating this process 'k' times so that each fold serves as the validation set exactly once. This method is crucial for both supervised and unsupervised learning as it helps in accurately estimating a model's predictive performance and preventing overfitting.
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