Consider the case of a training process where the model has balanced bias and variance:

In this graph, we can see that the polynomial regression model's output for the polynomial degree is equal to eight. The output values are close to both the training data and validation data. The following graph shows the MAE values during the training process:

We can see that the MAE value decreases consistently and that the predicted values for the training and validation data become close to the ground truth values. This means that the model's hyperparameters were good enough to balance bias and variance.