Cross-validation and bootstrap are two resampling methods used to evaluate statistical models and estimate generalization error and parameter variance, respectively. Cross-validation splits the data into folds and trains the model on each fold, using the remaining folds for validation. Bootstrap creates subsamples of the data with replacement and trains the model on each subsample. K-fold cross-validation is a resampling technique that splits the data into K folds and trains the model on K-1 folds, using the remaining fold for validation. This process is repeated K times, and the average performance of the model on the validation folds is used to evaluate its overall performance.

Suppose we have a dataset of images of cats and dogs, and we want to train a machine learning model to classify the images correctly. We can use K-fold cross-validation to evaluate the performance of our model. First, we split the dataset into K folds, where K is a positive integer. Then, we train the model on K-1 folds and evaluate its performance on the remaining fold. This process is repeated K times, and the average performance of the model on the validation folds is used to evaluate its overall performance. For example, if we choose K=5, we would split the dataset into 5 folds. Then, we would train the model on 4 folds and evaluate its performance on the remaining fold. This process would be repeated 5 times, and the average performance of the model on the validation folds would be used to evaluate its overall performance.