What does overfitting refer to in machine learning?

Overfitting in machine learning occurs when a model learns not just the underlying patterns in the training data but also the noise and outliers. This can happen when the model is too complex relative to the amount and noise of the training data, leading it to memorize rather than generalize.

When a model overfits, it shows exceptional performance on the training dataset but fails to perform well on new, unseen data, because it has tailored itself too closely to the specific examples it was trained on. In this sense, capturing noise distracts the model from gaining the true insights it needs to make accurate predictions on unfamiliar datasets.

Consequently, while the other choices touch on various aspects of model performance and behavior, only the correct one directly addresses the issue of overfitting by emphasizing the model's tendency to capture noise rather than the essential structure of the data.