Validation Curve Interpretation I have been reading about the validation_curve function from scikit learn. When I run this it takes too long. Therefore, I am plotting the results from grid search instead, which seems to be far quicker. However, when I plot using the code from: https://matthewbilyeu.com/blog/2019-02-05/validation-curve-plot-from-gridsearchcv-results.
I get:

Why are the lines so close together? Shouldn't I get a curve instead? More generally, how can I tell if my model is under-fitting or overfitting? How  can I plot the overfit/under-fit areas on my plot.
edit: i am trying to predict if a client will  default on a loan or not so 1 for yes 0 for no. the data are financials. also the data i have used is about 100k samples
You can understand whether your model is overfitting or underfitting  by the difference in the graph of your train and validation score. If your train score(performance not loss) is low and so is val score then your model is underfitting. On the other hand if your model is overfiiting you will have high training accuracy but your validation score will be low and the train and val graph will be far from each other. A perfect model just has high training score with the validation  curve as close as possible and the two graphs will be very close like the graph you provided.