How does overfitting affect a machine learning model's performance?

Overfitting occurs when a machine learning model learns the details and noise in the training data to the extent that it negatively impacts the model's performance on new, unseen data. When a model is overfitted, it has essentially become too complex, capturing patterns that do not generalize beyond the training set. As a result, while the model performs exceptionally well on the training data, its predictive accuracy deteriorates when encountering new data that it has not seen before.

This phenomenon is problematic because the primary goal of a machine learning model is to generalize well, meaning it can accurately predict outcomes on data that it hasn't been trained on. Thus, overfitting fundamentally limits the model's ability to function effectively in real-world scenarios where it must operate with new data.

Other options do not accurately reflect the impact of overfitting. For example, overfitting does not enhance generalization (which is the contrary of the first choice) nor does it facilitate quick learning from training data in a beneficial way (as suggested by the third choice). The last option also misrepresents the situation, as overfitting usually leads to memorization of data, including incorrect outputs, rather than preventing it.