What does overfitting in machine learning indicate?

Overfitting in machine learning refers to a situation where a model learns the training data too well, including its noise and outliers. As a result, while the model performs exceptionally well on the training dataset, it struggles to generalize when presented with new, unseen data. This is because the model has essentially memorized the training data rather than understanding the underlying patterns that define the task at hand.

When a model overfits, it signifies that it has become overly complex relative to the amount and nature of data available. This leads to inferior performance during validation or testing phases. For practitioners, recognizing overfitting is crucial as it highlights the need for strategies to improve the model's generalization capabilities, such as regularization techniques, simplifying the model architecture, or using more training data.

The indication of a model that cannot generalize well aligns directly with the concept of overfitting, making this the correct answer.