Explain the concept of overfitting in machine learning and how cross-validation helps in detecting and mitigating overfitting. Provide a detailed explanation and include a hypothetical example of a scenario where cross-validation would be crucial in preventing overfitting.

Simulated

Overfitting occurs when the model performs poorly on the training data, and cross-validation cannot help in this scenario.

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Overfitting occurs when the model performs too well on the training data but poorly on unseen data, and cross-validation helps by providing a more reliable estimate of model performance on unseen data.

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Overfitting is not a concern in machine learning, and cross-validation is used only for hyperparameter tuning.

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Overfitting is only relevant to neural network models and not to other types of machine learning models.

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Explain the concept of overfitting in machine learning and how cross-validation helps in detecting and mitigating overfitting. Provide a detailed explanation and include a hypothetical example of a scenario where cross-validation would be crucial in preventing overfitting.