Evaluation metrics for Decision Tree regressor and KNN regressor

Question

I have started working on the Decision Tree Regressor and KNN Regressor.

I have built the model and not sure what are the metrics needs to be considered for evaluation. As of now I have considered Root mean squared error.

Can we use $R^2$ values for Decision Tree and KNN or is it applicable only to Linear Regression Model

I have also pasted the $R^2$ and MSE values. As per my understanding Linear regression model is stable, Decision tree is over fitting and KNN is having less error. Which model needs to be considered here?

R2 Score for train - KNN regression 0.8215942683102192
R2 Score for test - KNN regression 0.7160388084850589
Mean squared error for train - KNN regression 49.92162362176166
Mean squared error for test - KNN regression 78.30907381395349

R2 Score for train - linear regression 0.6141419744748021
R2 Score for test - linear regression 0.6117893766210736
Mean squared error for train - linear regression 107.97107771851036
Mean squared error for test - linear regression 107.0583420197463

R2 Score for train - Decision Tree regression 0.9962039204515297
R2 Score for test - Decision Tree regression 0.7866182225490949
Mean squared error for train - Decision tree regression 1.0622217832469776
Mean squared error for test  - Decision tree regression 58.84511637596899

Updating the AIC and BIC values

AIC value - Test - KNN       :   -3.8272461328797505
BIC value - Test - KNN       :   1169.4748549110836
AIC value - Test - Linear Reg:   -4.452667046616746
BIC value - Test - Linear Reg:   1250.154152783156
AIC value - Test - Decision T:   -3.2766787253336602
BIC value - Test - Decision T:   1098.4516593376377

Thank you.