Select the statement that explains the concepts of training and testing in the context of machine learning.

Training is the initial stage where a model is evaluated using a separate dataset to ensure it meets performance standards. Testing comes later and focuses on enhancing a model's accuracy through parameter tuning.

Training encompasses the process of adjusting a model's parameters to minimize its error on a training dataset. Testing refers to evaluating the model's performance on new, unseen data to gauge its ability to generalize.

Training involves evaluating a model's performance on new, unseen data to ensure its generalization capability. Testing, on the other hand, refers to adjusting the model's parameters to minimize its training error.