<p>Proficiency in data analysis and modeling is increasingly essential across science, technology, engineering, and mathematics (STEM) disciplines. A critical component of these skills is model validation—assessing how well a model’s predictions align with empirical data and the theoretical assumptions underlying the model. This study examines the model validation competency of undergraduate STEM students as they engaged with two data-rich modeling tasks situated in different academic domains. Students employed a range of modeling strategies, most commonly developing black-box or white-box models, with some constructing grey-box models that integrated data and theoretical reasoning. The findings suggest that students’ validation approaches were shaped by specific features of the tasks, such as the availability of data and the degree of ambiguity in the problem context. The study highlights how data-rich tasks can stimulate validation activities, underscoring the importance of educators' deliberate selection of validation techniques to ensure tasks effectively achieve their intended objectives.</p>