In machine learning, what does 'model performance' indicate?

Model performance is fundamentally about how well a machine learning model can apply what it learned during its training phase to new, unseen data. This concept emphasizes the model's ability to generalize, which means making accurate predictions on data that were not part of the training process. Generalization is critical because a model that performs well on training data might not perform as effectively on new data due to overfitting, where it learns the noise and details rather than the underlying patterns.

Focusing on this aspect of model performance helps practitioners evaluate how well their models will fare in real-world scenarios, where they will encounter a diverse range of inputs unlike the data they were initially trained on. This is why option B stands out as the correct answer in understanding model performance in machine learning.