Question: Differentiate between overfitting and underfitting in the context of machine learning models.

Answer: Overfitting occurs when a model learns the training data too well, capturing noise and producing poor generalization on new data. Underfitting happens when a model is too simple to capture the underlying patterns in the data, resulting in poor performance on both training and test sets.