# How to interpret the Mean squared error value in a regression model?

I'm working on a simple linear regression model to predict 'Label' based on 'feature'. The two variables seems to be highly correlate corr=0.99. After splitting the data sample for to training and testing sets. I make predictions and evaluate the model.

metrics.mean_squared_error(Label_test,Label_Predicted) = 99.17777494521019
metrics.r2_score(Label_test,Label_Predicted) = 0.9909449021176512


Based on the r2_score my model is performing perfectly. 1 being the highest possible value. But when it comes to the mean squared error, I don't know if it shows that my model is performing well or not.

1. How can I interpret MSE here ?

2. If I had multiple algorithms and the same data sets, after computing MSE or RMSE for all models, how can I tell which one is better in describing the data ?

3. R2 score is 0.99, is this suspicious ? Or expected since the label and feature are highly correlated?

      Feature        Label
0   56171.757812    56180.234375
1   56352.500000    56363.476562
2   56312.539062    56310.859375
3   56432.539062    56437.460938
4   56190.859375    56199.882812
...     ...     ...
24897  56476.484375    56470.742188
24898  56432.148438    56432.968750
24899  56410.312500    56428.437500
24900  56541.093750    56541.015625
24901  56491.289062    56499.843750