In the context of preparing data for machine learning models, data normalization is a critical preprocessing step. Consider a scenario where you are working on a predictive model that includes features with vastly different scales, such as age (ranging from 0 to 100) and income (ranging from 20,000 to 200,000). The model's performance is suboptimal, and you suspect that the disparity in feature scales might be a contributing factor. Which of the following best describes the primary benefit of applying data normalization in this scenario? Choose the best option.