Explain the concept of feature selection in machine learning and its importance in model performance. How would you approach feature selection in a distributed environment using Spark ML, and what tools would you use to identify and select the most relevant features?

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Feature selection is unnecessary in distributed machine learning; Spark ML automatically selects features.

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Feature selection is crucial for reducing dimensionality and improving model performance; Spark ML provides tools like ChiSqSelector for feature selection.

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Feature selection is only relevant for small datasets; Spark ML uses direct feature inclusion for large datasets.

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Feature selection is handled by external algorithms; Spark ML integrates with these for feature selection.

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Explain the concept of feature selection in machine learning and its importance in model performance. How would you approach feature selection in a distributed environment using Spark ML, and what tools would you use to identify and select the most relevant features?