Prediction models are extensively used in numerous areas including clinical settings where a prediction model helps to detect or screen high-risk subjects for early interventions to prevent an adverse outcome, assist in medical decision-making to help both doctors and patients to make an informed choice regarding the treatment, and assist in healthcare services with planning and quality management. There are two main components of prediction modeling: model development and model validation. Once a model is developed using an appropriate modeling strategy, its utility is assessed through model validation. Model validation provides a true test of a model’s predictive ability when the model is applied on an independent data set. A model may show outstanding predictive accuracy in a dataset that was used to develop the model, but its predictive accuracy may decline radically when applied to a different dataset. In the era of precision health where disease prevention through early detection is highly encouraged, accurate prediction of a validated model has become even more important for successful screening. Different clinical practice guidelines also recommend incorporating only those prediction models in clinical practice that has demonstrated good predictive accuracy in multiple validation studies. Our purpose is to introduce the readers with the basic concept of model validation and illustrate the fundamental steps and procedures that are necessary to implement model validation.