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A data scientist performs hyperparameter tuning with k-fold cross-validation and then retrains the best model on the full training set. Using MLflow, they need to track metrics/hyperparameters per configuration, associate the final model with the search process, and enable easy comparison in the MLflow UI. Which run structure best satisfies these needs?

A Flat runs with tags to indicate hyperparameter sets and fold numbers, storing all metrics and models at the same level.
B Nested runs with a parent run for the whole experiment, child runs per hyperparameter set logging aggregated CV metrics, and the final model evaluation as a separate run of the best set.
C Nested runs with a parent run for each cross-validation fold and a child run for the best-performing hyperparameter set.
D Nested runs with a parent run for the whole experiment and child runs only for the final model training, logging all CV results as metrics in the parent run.

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