Regarding increase in MSE of Cross-Validation model with increasing dataset for regression

I have the following experimental setup for a regression problem.

Using the following routine, a data set of about 1800 entries is separated into three groups, validation, test, and training.

X_train, X_test, y_train, y_test = train_test_split(inputs, targets, test_size=0.2,
                                                    random_state=42, shuffle=True)

X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.25,
                                                  random_state=42, shuffle=True)

So in essence, training size ~ 1100, validation and test size ~ 350, and each subset is then having unique set of data points, that which is not seen in the other subsets.

With these subsets, I can preform a fitting using any number of the regression models available from scikit-learn, using the following routine:

model = LinearRegression()
clf = make_pipeline(StandardScaler(), model)
clf.fit(X_train, y_train)
predictions = clf.predict(X_test)

Doing this I then calculate the RMSE of the predictions, which in the case of the linear regressor, is about ~ 0.948.

Now, I could instead use cross-validation and not worry about splitting the data instead, using the following routine:

model = LinearRegression()
clf = make_pipeline(StandardScaler(), model)
predictions2 = cross_val_predict(clf, X, y, cv=KFold(n_splits=10, shuffle=True, random_state=42))

However, when I calculate the RMSE of these predictions, it is about ~2.4! To compare, I tried using a similar routine, but switched X for X_train, and y for y_train, i.e.,

model = LinearRegression()
clf = make_pipeline(StandardScaler(), model)
predictions3 = cross_val_predict(clf, X_train, y_train, cv=KFold(n_splits=10, shuffle=True, random_state=42))

and received a RMSE of about ~ 0.956.

I really do not understand why that when using the entire data set, the RMSE for the cross-validation is so much higher, and that the predictions are terrible in comparison to that with reduced data set.

Additional Notes

Additionally, I have tried out running the above routine, this time using the reduced subset X_val, y_val as inputs for the cross validation, and still receive small RMSE. Additionally, when I simply fit a model on the reduced subset X_val, y_val, and then make predictions on X_train, y_train, the RMSE is still better (lower) than that of the cross-validation RMSE!

This does not only happen for LinearRegressor, but also for RandomForrestRegressor, and others. I have additionally tried to change the random state in the splitting, as well as completely shuffling the data around before handing it to the train_test_split, but still, the same outcome occurs.

Edit 1.)

I tested out this on a make_regression data set from scikit and did not get the same results, but rather all the RMSE are small and similar. My guess is that is has to do with my data set. If anyone could help me out in understanding this, I would greatly appreciate it.

Edit 2.)

Hi thank you (@desertnaut) for the suggestions, the solution was actually quite easy, and the fact was that in my routine to process the data, I was using (targets, inputs) = (X, y), which is really wrong. I swapped that with (targets, inputs) = (y, X), and now the RMSE is about the same as the other profiles. I made a histogram profile of the data and found that problem. Thanks! I'll save the question for about 1 hour, then delete it.