What does 'overfitting' mean in the context of machine learning models?

In machine learning, overfitting refers to a situation where a model learns not only the underlying patterns in the training data but also the noise and random fluctuations present within that data. When a model overfits, it becomes excessively complex, capturing details that do not generalize well to unseen data. This generally leads to high performance on the training dataset but poor performance on any new or validation datasets, as the model fails to accurately predict outcomes based on patterns that are representative of the overall problem domain.

Understanding this is crucial, as the goal of developing a machine learning model is to achieve a balance between bias and variance, where the model is complex enough to learn the relevant patterns in the data without becoming overly complex and sensitive to noise. The other options do not accurately describe overfitting. For example, a model that generalizes well is the opposite of overfitting, while an underdeveloped model would likely lack sufficient complexity to learn even the important patterns in the data. Likewise, a model optimized for speed rather than accuracy might function differently but does not inherently relate to the challenge of overfitting.