Which function can be used for evaluating predicted values against actual data in various models?

The function designed specifically for evaluating predicted values against actual data in various models is ML.EVALUATE. This function typically provides a comprehensive way to assess the performance of a model by comparing the predictions made by the model to the actual outcomes within the dataset.

Evaluation metrics are crucial for understanding how well a model is performing, and ML.EVALUATE often incorporates different statistical measures such as accuracy, precision, recall, F1 score, and others, depending on the type of model and the problem being solved. This evaluation helps in determining not just how well the model fits the training data, but also its generalization capabilities on unseen data.

In contrast, functions like ML.FIT are generally used for training models, where the focus is on learning from the training data. ML.PREDICT is aimed at generating predictions based on the trained model and a new dataset, while ML.SCORE typically references a specific metric of the model's performance but is less comprehensive than ML.EVALUATE in terms of providing a robust analysis across multiple evaluation metrics. Hence, ML.EVALUATE is the most appropriate choice for the purpose described in the question.