Data cleaning is a foundational step in the machine learning pipeline, especially critical in scenarios like predicting customer churn where data quality directly impacts model accuracy. The correct options highlight its role in ensuring data accuracy and reliability (B) and in enhancing model performance through normalization and outlier removal (E).

• B: Correcting errors and handling missing values are essential to prevent the model from learning from inaccurate or incomplete data.
• E: Normalizing data and removing outliers are crucial for models sensitive to the scale of input features and for preventing skewed predictions.

Incorrect Options Analysis:

• A: While removing unnecessary columns can reduce computational overhead, it's not the primary purpose of data cleaning.
• C: Data cleaning does not replace the need for feature engineering; both are distinct and necessary steps.
• D: Compliance with data protection regulations requires more than just data cleaning; it involves a comprehensive data governance strategy.