GridSearchCV gives different result - python-3.x

Question

I have applied a gridsearchCV with estimators of DecisionTreeClassifier, RandomForestClassifier, LogisticRegression, XGBClassifier used all of them in ensemble learning.
The result that gridSearchCV will give with all these estimators are different in my system and my friend's system with same data of testing and training, I don't know why?
We are using same data for training and testing but gridsearch is giving different result for these data in both the system, just want to know what should be changed to make the system to give same result on any system?
gs_dt = GridSearchCV(estimator=DecisionTreeClassifier(random_state=42,class_weight={1:10, 0:1}),
param_grid=[{'max_depth': [ 2, 4, 6, 8, 10],
'criterion':['gini','entropy'],
"max_features":["auto", None],
"max_leaf_nodes":[10,20,30,40]}],
scoring=scoring,
cv=10,
refit='recall')
gs_rf = GridSearchCV(estimator=RandomForestClassifier(n_jobs=-1, oob_score = True,class_weight={1: 10/11, 0: 1/11}),
param_grid=[{'max_depth': [4, 6, 8, 10, 12, 16, 20, None],
'max_features': ['auto', 'sqrt'],
'min_samples_leaf': [2, 4, 8],
'min_samples_split': [10, 20]}],
scoring=scoring,
cv=10,
n_jobs=4,
refit='recall')
gs_lr = GridSearchCV(estimator=LogisticRegression(multi_class='ovr',random_state=42,class_weight={1:10, 0:1}),
param_grid=[{'C': [0.000001, 0.00001, 0.0001, 0.001, 0.01, 0.1 ,1],
'penalty':['l1','l2']}],
scoring=scoring,
cv=10,
refit='recall')
gs_gb = GridSearchCV(estimator=XGBClassifier(n_jobs=-1),
param_grid=[{'learning_rate': [0.01, 0.05, 0.1, 0.2],
'max_depth': [4, 6, 8, 10, 12, 16, 20],
'min_samples_leaf': [4, 8, 12, 16, 20],
'max_features': ['auto', 'sqrt']}],
scoring=scoring,
cv=10,
n_jobs=4,
refit='recall')
For example first gridsearchcv gives this result on my system:
DecisionTreeClassifier(class_weight={1: 10, 0: 1}, criterion='gini',
max_depth=8, max_features=None, max_leaf_nodes=10,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False, random_state=42,
splitter='best')
And on my friend's system it gives:
DecisionTreeClassifier(class_weight={0: 1, 1: 10}, criterion='gini',
max_depth=10, max_features=None, max_leaf_nodes=10,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort=False,
random_state=42, splitter='best')
Similarly I got different result on my and my friend's system.