To handle missing values in a large dataset, I would first use methods like isnull() and sum() to identify the extent of missing data. Depending on the situation, I could use imputation techniques like mean or median substitution, or drop the rows/columns if they have excessive missing values, ensuring that this decision aligns with the model’s requirements.