The sample means from the training data set are applied to the validation and test data sets. This is to ensure that the imputation strategy appropriately reflects the training data, which is the basis for model learning. Applying the mean imputation from the training set to the validation and test sets avoids data leakage and ensures a fair assessment of the model performance on unseen data.