What does training a model require in terms of data?

Training a machine learning model requires large amounts of diverse and representative data to ensure that the model generalizes well to unseen data. This is crucial because the model learns patterns and relationships from the training data, which should cover a wide range of scenarios it might encounter in real-world applications.

Diversity in the data helps the model to be robust against variations and complexities in the data distribution, while representative data ensures that the training process captures the true distribution of the problem space. If the training data is not representative, the model risks being biased or unable to perform well on data that differs from what it was trained on.

Having large datasets typically helps in reducing overfitting, particularly in complex models or deep learning architectures, where the risk of memorizing training examples is higher. Therefore, option C accurately reflects the requirements for effective model training.