Model assumptions are foundational premises that underpin the validity of a statistical or machine learning model, influencing its predictions and interpretations. Understanding and validating these assumptions is crucial, as violations can lead to biased, inconsistent, or inefficient estimates, impacting the model's reliability and applicability.