Machine learning plays a crucial role in various research areas and industries. The effectiveness of machine learning models relies heavily on the quality and quantity of training data. To evaluate model performance on unseen data, it is important to divide the data into training and testing datasets. A three-way split into train–validation–test datasets is also commonly used to create robust and generalized models. The validation set helps in tuning hyper-parameters to mitigate the problem of overfitting. It is of utmost importance to achieve a precise and true portrayal of data across all three categories of datasets: training, testing, and validation. Previous research has explored various statistical techniques such as “Split” aimed to ensure proper membership of the complete data in the test set. Despite the utilization of these techniques, insufficient evidence exists regarding the equitable treatment of the validation set. Although cross-validation is widely used for validation, randomly selecting the validation part may not be the complete representative of overall data, hindering the creation of a generalized model suitable for the test data. This work focuses on extracting validation sets using the Support Points (SPs) method in “Split” to obtain accurate data membership. Results demonstrate significant accuracy improvement when both test and validation sets are selected using the Support Points method.