Option D is correct because employing linear regression to estimate missing values ensures the data's accuracy without introducing bias, aligning with the need for a cost-effective and scalable solution. Linear regression, a supervised learning method, fits a linear equation to the data, enabling the prediction of unknown values. This technique is particularly useful for maintaining the model's integrity when dealing with low-variance features that are missing data. Option E is also correct as it suggests a comprehensive approach that not only addresses the missing data issue but also enhances the feature's variance, potentially improving the model's predictive performance. However, the primary focus should be on accurately handling the missing data, making D the best single option.