[Python] Evaluate transformations with the same model in scikit-learn

I would like to perform a regression analysis and test different transformations of the input variables for the same model. To accomplish this, I created a dictionary with the different pipelines, which I loop through:

import numpy as np
import pandas as pd
from sklearn.preprocessing import FunctionTransformer
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import make_pipeline
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import PowerTransformer
from sklearn.compose import TransformedTargetRegressor

# Define transformations and models
models = {
'linear': LinearRegression(),
'power': make_pipeline(PowerTransformer(), LinearRegression()),
'log': make_pipeline(FunctionTransformer(np.log, np.exp),
LinearRegression()),
'log-sqrt': TransformedTargetRegressor(
regressor=make_pipeline(
FunctionTransformer(np.log, np.exp),
LinearRegression()),
func=np.sqrt,
inverse_func=np.square
)
}

parameters = pd.DataFrame()
for name, model in models.items():
model.fit(x_train, y_train)
y_hat = model.predict(x_hat)
y_hat_train = model.predict(x_train)
r2 = model.score(x_train, y_train)
parameters.at[name, 'MSE'] = mean_squared_error(y_train, y_hat_train)
parameters.at[name, 'R2'] = r2
best_model = parameters['R2'].idxmax()


This works. However, there is probably a more elegant solution similar to GridSearchCV for evaluating models. Can anyone give me a tip on what I should look for?

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