Training, Cross Validation, and Test sets are all typical best practices. This allows you to fine-tune the algorithm's parameters without making decisions based solely on training data.
Splitting a dataset may also be useful for determining whether your model is suffering from one of two extremely prevalent problems known as underfitting or overfitting. Underfitting is typically caused by a model's inability to contain the relationships between data.
Data splits are important in machine learning because they help in improving the quality of a model by providing an opportunity for evaluation against another set of data that were not used to train it. Splitting data into two sets helps in understanding how well it was trained, which helps in making better decisions about what type of model should be built next time around.
The three most common split methods are:
Stratified sampling - this method involves splitting the data into two groups, then taking a random sample from each group;
Random sampling - this method involves taking a random sample from each group; and
Bootstrapping - this method involves taking an initial sample from each group and then re-sampling with replacement until all groups have been sampled.