It simplifies the dataset by removing unnecessary columns, thereby reducing computational overhead.

It ensures the accuracy and reliability of the dataset by correcting errors and handling missing values.

It eliminates the need for feature engineering by automatically selecting the most relevant features.

It guarantees the dataset's compliance with global data protection regulations without further checks.

It improves the model's performance by normalizing data scales and removing outliers that could skew predictions.