ML: Classification Model Comparison Given is a dataset that I need to use for a classification and I want to compare the performance of different classification models. 
Let's assume, I want to look at logistic regression (with different cut-off-points) and KNN.
Is there anything problematic if I proceed as follows:


Split data in training and validation data (and a test set for the performance evaluation of the winning model).
Train a logistic regression model and a KNN classification model on the training set.
I consider for each cut-off point t between 0 and 1 the logistic regression model as a classification model - so the regression model leads to many classification models.
I now compare for a certain range of t (lets say 0.01 to 0.99) the classification performance of all my classification models (logistic regression for those t and KNN) on the validation data.
The one with the best performance (based on a certain metric) I'll choose.


I was discussing this with somebody else who argued that t needs to be considered as hyperparameter and this parameter needs to be tuned separately. 
If this is true - why? And what's wrong with my arguments above?
In principle you can use your approach.

However, you should not optimize on your test set (step 3). Instead you should select the best t using your validation set.
Then you compare it against KNN, also on the validation set. Finally, the best model should be evaluated on the test set.