Experiment tracking records, for each training run, the parameters, metrics, data used, code version and artifacts produced. It lets you compare attempts, find out how a model was obtained and reproduce a result. MLflow is a common tool for this.
Example
mlflow.log_param('max_depth', 6)
mlflow.log_metric('f1', 0.81)