Some common pitfalls to avoid during model selection in machine learning are:
Overfitting to the training data,
Underfitting,
Using an evaluation metric that is not appropriate for the problem at hand,
Ignoring the interpretability and transparency of the model,
Not performing appropriate feature selection and engineering, and
Failing to consider the scalability and computational complexity of the model.