Applying design principles has long been acknowledged as beneficial for understanding and maintainability in traditional software projects. These benefits may similarly hold for machine learning (ML) projects, which involve iterative experimentation with data, models, and algorithms. However, ML components are often developed by data scientists with diverse educational backgrounds, potentially resulting in code that doesn t adhere to software development best practices. In order to better understand this phenomenon, we investigated the impact of the SOLID design principles on ML code understanding. To this end, we conducted a controlled experiment with three independent trials (exact replications), overall involving 100 data scientists. We restructured ML code from a real industrial setting that did not use SOLID principles. Within each trial, one group was presented with the original ML code, while the other one was presented with ML code incorporating SOLID principles. Participants of both groups were asked to analyze the code and fill out a questionnaire that included both open-ended and closed-ended questions on their understanding. The study results provide statistically significant evidence that the adoption of the SOLID design principles can improve code understanding within the realm of ML projects. We put forward that software engineering design principles should be spread within the data science community and considered for enhancing the maintainability of ML code.