Putting together sklearn pipeline+nested cross-validation for KNN regression

I'm trying to figure out how to built a workflow for sklearn.neighbors.KNeighborsRegressor that includes:

• normalize features
• feature selection (best subset of 20 numeric features, no specific total)
• cross-validates hyperparameter K in range 1 to 20
• cross-validates model
• uses RMSE as error metric

There's so many different options in scikit-learn that I'm a bit overwhelmed trying to decide which classes I need.

Besides sklearn.neighbors.KNeighborsRegressor, I think I need:

sklearn.pipeline.Pipeline  
sklearn.preprocessing.Normalizer
sklearn.model_selection.GridSearchCV
sklearn.model_selection.cross_val_score

sklearn.feature_selection.selectKBest
OR
sklearn.feature_selection.SelectFromModel

Would someone please show me what defining this pipeline/workflow might look like? I think it should be something like this:

import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import Normalizer
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import cross_val_score, GridSearchCV

# build regression pipeline
pipeline = Pipeline([('normalize', Normalizer()),
                     ('kbest', SelectKBest(f_classif)),
                     ('regressor', KNeighborsRegressor())])

# try knn__n_neighbors from 1 to 20, and feature count from 1 to len(features)
parameters = {'kbest__k':  list(range(1, X.shape[1]+1)),
              'regressor__n_neighbors': list(range(1,21))}

# outer cross-validation on model, inner cross-validation on hyperparameters
scores = cross_val_score(GridSearchCV(pipeline, parameters, scoring="neg_mean_squared_error", cv=10), 
                         X, y, cv=10, scoring="neg_mean_squared_error", verbose=2)

rmses = np.abs(scores)**(1/2)
avg_rmse = np.mean(rmses)
print(avg_rmse)

It doesn't seem to error out, but a few of my concerns are:

• Did I perform the nested cross-validation properly so that my RMSE is unbiased?
• If I want the final model to be selected according to the best RMSE, am I supposed to use scoring="neg_mean_squared_error" for both cross_val_score and GridSearchCV?
• Is SelectKBest, f_classif the best option to use for selecting features for the KNeighborsRegressor model?
• How can I see:
  • which subset of features was selected as best
  • which K was selected as best

Any help is greatly appreciated!