Correct Option: D. To scale the features to a uniform range, thereby improving the model's ability to learn from each feature equally.

Explanation: Data normalization is crucial in machine learning for scaling numerical features to a common range, such as 0 to 1 or -1 to 1. This process is particularly beneficial in scenarios like predicting housing prices, where features like square footage and number of bedrooms vary widely in scale. Normalization ensures that each feature contributes equally to the model's learning process, leading to more accurate and efficient model performance.

Why other options are not correct:

• A. To encrypt sensitive information within the dataset, enhancing privacy and security: Normalization does not involve encryption or directly enhance data security.
• B. To reduce the storage space required by eliminating redundant data entries: Normalization is unrelated to data deduplication or storage optimization.
• C. To ensure that all data entries follow a consistent format, facilitating easier data merging from multiple sources: While normalization makes features comparable, it does not standardize data formats or resolve inconsistencies in data merging.
• E. Both A and C are correct: Since neither A nor C accurately describes the primary benefit of data normalization, this option is incorrect.