Model validation is an essential stage in the machine learning lifecycle, ensuring models generalize well to unseen data by evaluating their predictions independently from the training dataset. This process helps identify overfitting, where a model learns noise instead of the signal, and underfitting, where it is too simplistic. Techniques like the holdout method, cross-validation, and bootstrapping are crucial in validating model performance, providing insights into how models might perform on unseen data. A data-centric approach is emphasized for its role in improving model reliability by focusing on data quality, including accurate labeling and validation, which is particularly crucial in computer vision (CV) applications. Encord Active, a data-centric platform, is highlighted for its ability to validate models, such as a pre-trained Mask R-CNN for COVID-19 scans, by providing AI-assisted evaluation features and tools for manually inspecting models' real-world performance. It offers comprehensive metrics and supports human-in-the-loop validation, ensuring a model's interpretations align with human expertise. Selecting the right model validation tools involves considering factors such as data specificity, robust data validation, comprehensive evaluation metrics, and flexibility, which are vital for ensuring models perform accurately and efficiently, especially in critical sectors like healthcare imaging and autonomous driving.