Overfitting occurs when the model performs poorly on the training data, and cross-validation cannot help in this scenario.

Overfitting occurs when the model performs too well on the training data but poorly on unseen data, and cross-validation helps by providing a more reliable estimate of model performance on unseen data.

Overfitting is not a concern in machine learning, and cross-validation is used only for hyperparameter tuning.

Overfitting is only relevant to neural network models and not to other types of machine learning models.