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When building a model, the data scientist can set the value of hyperparameters for the model. Examples of hyperparameters are the number of layers in an artificial neural network, the number of trees in a random forest, etc. The modeler has the power to decide these hyperparameters, providing the flexibility to train the best model. Flexibility comes at the expense of added complexity. So many choices can be overwhelming.