Answer: Data cleaning ensures the dataset is free from errors and inconsistencies, which is crucial for training accurate models.

**Correct Option:** C. Data cleaning ensures the dataset is free from errors and inconsistencies, which is crucial for training accurate models: This is correct because data cleaning involves handling missing values, removing duplicates, and correcting inconsistencies. These steps are essential to ensure that the machine learning model is trained on high-quality data, leading to more accurate predictions. High-quality data is particularly important in scenarios like predicting customer churn, where the cost of inaccurate predictions can be significant. **Incorrect Options:** A. Data cleaning introduces variability in the dataset, making the model more robust to unseen data: This is incorrect because the primary goal of data cleaning is not to introduce variability but to remove noise and errors from the dataset. B. Data cleaning simplifies the dataset by removing unnecessary features, thus reducing computational costs: This is incorrect because while data cleaning may involve removing irrelevant data, its main purpose is to improve data quality, not necessarily to reduce computational costs. D. Data cleaning automates the feature selection process, eliminating the need for manual intervention: This is incorrect because data cleaning and feature selection are distinct processes. Data cleaning focuses on improving data quality, whereas feature selection involves choosing the most relevant features for the model.

Author: LeetQuiz Editorial Team