Is it possible in practice to completely avoid overfitting?

I’m not sure if this fits in here, so feel free to delete, but I had a weird thought when I as thinking about test data vs validation data.

So, typically data is split up into training data, validation data and test data. I use the training data to construct the model, validation data to tune the parameters, and test data to determine the accuracy of the model. The reason why only having training data and validation data is insufficient …

construct data datascience free overfitting practice test thinking thought training training data validation