Validation Curve Interpretations for Decision Tree
A few comments on the top of my head:


both parameters min_samples_leaf and max_depth are not very important for decision trees, so it's not surprising not to see much variation (or not all) across different values: 


the fact that min_sample_leaf doesn't influence the performance simply means that the algorithm finds enough good predictors in the features to create leaves with a high number of instances, apparently always more than 30.
I'm less sure about max_depth, but I assume that it's a parameter which is used to prune the tree in order to avoid overfitting. This is exactly what happens here as soon as it's increased above 5: the algorithm creates a deeper (more complex) tree by using very specific conditions on the features which turn out to be specific to the training set, hence the divergence between training score and CV score (that's a clear sign of overfitting).

Value 5 for max_depth is indeed optimal, as seen on the second graph (due to overfitting with higher values). The value 19 returned by grid search for min_samples_leaf is as optimal as any other value between 1 and 30, as seen on the first graph: grid search just happened to pick this one but it doesn't have any impact anyway.
The 82% accuracy is completely normal: that's the Y axis on both graphs ;) It indeed looks like a decent performance, but we can't say for sure since there's no comparison: maybe the dataset is super easy and 82% is just the majority class, or maybe it's super hard and 82% is a great achievement.


Actually these graphs don't show much about the learning, they just show that the parameters studied are not really relevant. To observe something more interesting try:


Ablation study: pick a random subset of the training set instances and train only with this subset. Do this for say 10%, 20%, ..., 100% of the data, then plot the performance as a function of the size of the training set. This will show how many instances are needed for the model to reach its max performance (educated guess: not that many).
Feature selection: use only the N most informative features (as measured by information gain for instance) and plot the performance for different values of N. If you're lucky you might see an increase until a particular optimal value of N followed by a small decrease when uninformative features are added. If it happens, that last part would be due to overfitting.