Discuss the importance of data preprocessing in distributed machine learning and how Spark ML facilitates this process. Provide an example of a common preprocessing step and how it can be implemented in Spark ML.

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Data preprocessing is unnecessary in distributed machine learning; Spark ML focuses on model training.

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Data preprocessing is crucial for ensuring data quality and model performance; Spark ML provides built-in functions like StringIndexer for categorical data handling.

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Data preprocessing is only needed for small datasets; Spark ML uses direct data ingestion for large datasets.

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Data preprocessing is handled by external tools; Spark ML integrates with these tools for preprocessing.

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Discuss the importance of data preprocessing in distributed machine learning and how Spark ML facilitates this process. Provide an example of a common preprocessing step and how it can be implemented in Spark ML.