Describe the process of model selection using cross-validation and grid-search. Include details on how to implement this process in a machine learning pipeline using Python and the scikit-learn library, and explain the benefits of this approach.

Simulated

Model selection involves choosing the first model that shows acceptable performance without any further tuning.

5.6%

Model selection using cross-validation and grid-search involves systematically testing different models and hyperparameters to find the best combination for the given dataset.

94.4%

Model selection is a manual process that does not require any computational tools or libraries.

0.0%

Model selection is only applicable to simple models and not to complex machine learning models.

0.0%

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Describe the process of model selection using cross-validation and grid-search. Include details on how to implement this process in a machine learning pipeline using Python and the scikit-learn library, and explain the benefits of this approach.