While several ML algorithms are available to build models, model selection can be done on the basis of the bias and variance errors that the models produce.

Bias error occurs when the model has a limited capability to learn the true signals from a dataset provided as input to it. Having a highly biased model essentially means the model is consistent but inaccurate on average.

Variance errors occur when the models are too sensitive to the training datasets with which they are trained. Having high variance in a model essentially means that the trained model will produce high accuracies on any test dataset on average, but their predictions are inconsistent.